{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LeNet on Cifar with Dropout (0.5)\n",
    "\n",
    "This is LeNet (6c-16c-120-84) on MNIST. Adam algorithm (lr=0.001) with 100 epoches.\n",
    "\n",
    "\n",
    "#### LeNet\n",
    "\n",
    "    Total params: 44,426\n",
    "    Trainable params: 44,426\n",
    "    Non-trainable params: 0\n",
    "\n",
    "\n",
    "####  LeNet with 10 intrinsic dim\n",
    "\n",
    "    Total params: 488,696\n",
    "    Trainable params: 10\n",
    "    Non-trainable params: 488,686\n",
    "    \n",
    "#### LeNet with 20000 intrinsic dim    \n",
    "    Total params: 888,584,426\n",
    "    Trainable params: 20,000\n",
    "    Non-trainable params: 888,564,426  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_num(lines0):\n",
    "\n",
    "    valid_loss_str     = lines0[-5]\n",
    "    valid_accuracy_str = lines0[-6]\n",
    "    train_loss_str     = lines0[-8]\n",
    "    train_accuracy_str = lines0[-9]\n",
    "    run_time_str       = lines0[-10]\n",
    "\n",
    "    valid_loss     = float(valid_loss_str.split( )[-1])\n",
    "    valid_accuracy = float(valid_accuracy_str.split( )[-1])\n",
    "    train_loss     = float(train_loss_str.split( )[-1])\n",
    "    train_accuracy = float(train_accuracy_str.split( )[-1])\n",
    "    run_time       = float(run_time_str.split( )[-1])\n",
    "    \n",
    "    return valid_loss, valid_accuracy, train_loss, train_accuracy, run_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline LeNet:\n",
      "(1.05339, 0.6339, 1.19131, 0.58802, 365.401)\n",
      "\n",
      "10 dim:\n",
      "(3.13658, 0.0989, 7.05503, 0.10036, 398.862)\n",
      "\n",
      "50 dim:\n",
      "(2.57586, 0.0929, 4.83271, 0.09798, 394.109)\n",
      "\n",
      "100 dim:\n",
      "(2.46547, 0.0955, 4.17996, 0.09816, 369.779)\n",
      "\n",
      "500 dim:\n",
      "(2.2005, 0.1863, 2.42888, 0.12918, 379.746)\n",
      "\n",
      "1000 dim:\n",
      "(1.96199, 0.2754, 2.14893, 0.18754, 409.556)\n",
      "\n",
      "2000 dim:\n",
      "(1.78029, 0.3273, 1.94858, 0.24642, 490.971)\n",
      "\n",
      "5000 dim:\n",
      "(1.59584, 0.4194, 1.74932, 0.34194, 792.791)\n",
      "\n",
      "10000 dim:\n",
      "(1.40048, 0.4959, 1.5803, 0.42144, 1210.99)\n",
      "\n",
      "15000 dim:\n",
      "(1.3509, 0.52, 1.50944, 0.45548, 1748.32)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "results_dir = '../results/lenet_cifar_dropout'\n",
    "\n",
    "dim = [10,50,100,500,1000,2000,5000,10000,15000]\n",
    "i = 0        \n",
    "\n",
    "# filename list of diary\n",
    "diary_names = []\n",
    "for subdir, dirs, files in os.walk(results_dir):\n",
    "    for file in files:\n",
    "        if file == 'diary':\n",
    "            fname = os.path.join(subdir, file)\n",
    "            diary_names.append(fname)\n",
    "\n",
    "diary_names = sorted(diary_names)\n",
    "\n",
    "i = 0  \n",
    "diary_names_aug = []\n",
    "for dn in diary_names:\n",
    "    if '_dir' not in dn:\n",
    "        dn_new = dn\n",
    "        # dn_new = dn[:-6]  + str(dim[i])+ '_dropout' + dn[-6:]\n",
    "        i +=1\n",
    "        diary_names_aug.append(dn_new)\n",
    "    else:\n",
    "        diary_names_dir = dn\n",
    "\n",
    "diary_names_ordered=diary_names_aug  \n",
    "          \n",
    "# extrinsic update  method\n",
    "with open(diary_names_dir,'r') as ff:\n",
    "    lines0 = ff.readlines()\n",
    "    R_dir = extract_num(lines0)\n",
    "\n",
    "\n",
    "print \"Baseline LeNet:\\n\" + str(R_dir) + \"\\n\"\n",
    "\n",
    "\n",
    "# intrinsic update method\n",
    "Rs = []\n",
    "i = 0\n",
    "for fname in diary_names_ordered:\n",
    "    with open(fname,'r') as ff:\n",
    "        lines0 = ff.readlines()\n",
    "        R = extract_num(lines0)\n",
    "        print \"%d dim:\\n\"%dim[i] + str(R) + \"\\n\"\n",
    "        i += 1\n",
    "\n",
    "        Rs.append(R)\n",
    "                            \n",
    "Rs = np.array(Rs)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance comparison with Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YrrnmGj755BOGDh1KWVkZhYWFDB8+vF3Haa/4CdbqsxYRaVUkp8isra3l6KOPbrEpfNeu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f59bb014f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = len(dim)\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, Rs[:,0],'b-', label=\"Testing\")\n",
    "ax.plot(dim, R_dir[0]*np.ones(N),'r-', label=\"Testing: baseline\")\n",
    "ax.plot(dim, Rs[:,2],'g-', label=\"Training\")\n",
    "ax.plot(dim, R_dir[2]*np.ones(N),'y-', label=\"Training: baseline\")\n",
    "\n",
    "ax.scatter(dim, Rs[:,0])\n",
    "ax.scatter(dim, Rs[:,2])\n",
    "\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss')\n",
    "ax.set_title('Cross Entropy  Loss')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "# ax.set_ylim([-0.1,3.1])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E763PnsM1SUqozsmkmgSk1yYuLoj4+CDi4oKIi6tOenrBe2UFBWURGppGaGg6ISHphIam\nExqalud5OiEhua/XqZNBQDltu+Uvv6u8/KVMAwYMWK+qEd5c6+Z3ulggLM9xS3IHxAGgqkfzHM4F\nni3oRqr6KvAqQEREhLrZRBQdHe13TVBWpsrBylTxxMfDN9/AQ8/tImVPQ9IP1zvldameQesW1XKa\nugurRWfXpOvUCUCkJlDTNwUqQmX/XRXEH8tUHDeT+lqgvYicg5PMxwA35r1ARM5S1f2ew2HALy7G\nY4wxRUpOhpUrYdkyWLoU1q1zRo4HVGtF9RbHqN33V/7Y/xALDqcTWCuNlo1qsHLCaY2LxviMa0ld\nVTNEZBywGGdK2zxV/VlEpgLrVHUhcL+IDAMygGPAH92Kxxhj8ktPh7VrnQS+bBmsWuVM5apWDXr0\ngEmTYOBAOFTzAI9/4fSpt+mQQbXUahVi9TBj8is2qYtIoKoWMhmiaKq6CFiU79xjeZ5PBCaW5t7G\nGFNSWVnw449OAl+2zGlaT0pypmiFh8N99zlJ/NJLnaleuVpQvYazehgk0qKCrB5mTH7e1NS3iciH\nwBsFzDE3xpgKSxV++y23OT0qytkUBJxFU265BQYNgv79nZHjRclePSw6Opr7burvdujGlIo3Sb0L\nTn/4XBEJAOYB81U1wdXIjDGmFPbuzW1OX7YMYj3Dc8PCYNgwpyY+cCC0sEq28UPFJnVVTQReA14T\nkX7Au8A/ReQDYJqqbnc5RmOMKdThw04NPDuRb/f8j9SokZO8Bw1yfrZtayuhGf/nVZ86cCVwG9Aa\neB54B7gUp7+8g4vxGWPMKRISYPny3Jr4pk3O+bp1nWb0e+91kviFF1Juc7yNqSi86lMHooCZqroq\nz/kPPCvBGWOMa5KTnVHpeaeZZWY6G4v06QNPPeUk8e7dbTU1Y7z5J9BZVQtcxFBV7y/jeIwxVVz2\nNLPsmviqVZCa6iyZ2rMnTJzoJPHevZ3EbozJ5U1Sf0lE/qKqcQAiUh94XlVvdzc0Y0xVkJXlNKFn\n18Szp5mBM83s3nudfvHTp5kZY/LztqYel32gqsdFpKuLMRlj/JgqbNuWO7AtKsrZGhScpVZvucWp\niffv7wx2M8Z4z5ukHiAi9VX1OICINPDyfcYYAzjTzLJr4nmnmbVsCVdd5dTEBwxwjo0xpedNcn4e\nWC0iCwABrgemuxqVMaZSy55m9vbbHRg71qmZQ+40s+xHu3Y2zcyYsuTNPPX/E5H1wADPqetsZTlj\nTF4JCU5feHZtPHuaWa1aTRg4EO65x6mN2zQzY9zlVTO6ZyOWw3j2CxSRVqq6x9XIjDEVVnIyrF6d\n25y+dq0zzaxGDWea2fTpTk38xImVDBrUz9fhGlNleLP4zDCcJvjmwCHgbJwtUi9wNzRjTEWRkZE7\nzWzp0lOnmfXoARMmODXx/NPMoqPVd0Eb47ZN78PSqRAfAyEtYdBj0HmUT0PypqY+DegFLFHVriIy\nALjZ3bCMMb6UlQWbN+fWxL/5BhITnde6dMldte3SS6FePd/GaoxPbHofPrufxPSTHEJpG78XPvMs\n3eLDxO5NUk9X1aMiEiAiAaoaJSKzXI/MGFNusqeZZS/4EhUFR444r3XoADfd5CTxAQNsmpmpOtIy\n04hNiGVvwl72xO9hb7znZ8Je9uxYwp7MVOIFGqhwlLqQnuzU3Ct4Uo8TkTrAN8A7InIIOOFuWMYY\nt8XEnDrNLCbGOd+iBVxxRe40s7Aw38ZpjBtUlUMnDuUk7JyknZCbvA8kHUA5tQupYXBDWoW04pzM\nTPoRRJgG0IoAFEUQpyneh7xJ6tcAycBfgZuAEGCqm0EZY8rekSNODTw7kWdPM2vYMHeK2aBBNs3M\n+IfE1ER2ndhFyvaUAhN2TEIMqZmpp7wnuFowrUJa0SqkFZe3u5xWIa0ICwlzftYLIywkjFpBtZyL\n/3khxO89/YNDfLvYQpFJ3bND2+eqOgDIAt4sl6iMMWcsIQFWrMitif/4o3O+Th3o18+ZZjZwIHTq\nZNPMTOVSZLO4p9YdnxrvXLzO+REogTSv25xWIa24qMVFjDhvxCkJu1VIKxoEN0C8/UY76DGnDz09\nOfdcULBz3oeKTOqqmikiWSISoqrx5RWUMabkUlJydzNbtgzWrDl1mtmTTzpJPCICgoJ8Ha0xBcvf\nLJ6dsL1uFq9/Dv3O7kdYSBhJsUkM6TWEViGtOKvuWVQLKMPFULP7zSvh6PckYLOIfE2evnTboc0Y\n38rIcLYhzW5OX7kyd5rZRRc508yydzMLDvZ1tMY4ElMTi+zHLqpZPCwkjMvbXX5aDfuUZvE8oqOj\n6dOqj3uF6TzK50k8P2+S+keehzHGh7Ky4KefcpvTly8/dZrZn//sJPG+fW2amfGN4prF9ybsJS4l\n7pT3BEgALeq2ICwkjItaXMR1512X069dqmbxKs6bZWKtH90YH1CF7dtza+J5p5m1bw833ugMbOvf\nHxo39mmopgoorFk8b627sGbxsJCwU5rF8ybsMm8Wr+K8WVFuF3DaslCq2saViIypwmJjc2viy5Y5\nu5tB7jSz7FHqNs3MlLWTGSfZcnhLof3YpWkWb1mvJbWr1/ZRiaomb74eReR5XhMYCTRwJxxjqpYj\nRyA62kngn3/eIyeJN2zozBF/5BEnibdvb9PMTOl53Sy+Mvc9hTWL5+3Hbhjc0JrFKxhvmt+P5js1\ny7Nrm2/H7RtTCSUmnjrNbONG53ydOnDhhcmMH1+LgQOhc2ebZma8U1yz+N6EvexP3F9ks3jfs/uS\nfiSdAd0GWLN4JedN83u3PIcBODV3+00b44WUFGc3s7zTzDIynGlmF18M06Y5/eIREbBy5Wb69+/v\n65BNBZN3tHhJmsWzm8KHtB1y2sCzgprFo6Oj6X9h/3IsmXGDN8n5+TzPM4BdQMUaw29MBZGRAevX\nnzrNLCXFqXVfdBH8/e9Oc/rFF9s0MwPpmenEJsaeOr3Li9HieRdRsWZxk5c3ze8DyiMQYyqjrCz4\n+edTp5klJDivde4Md9/t1MQvvRRCQnwbqzlD2dtsNrsT/jmu2IVGyqpZPH/Cbl63uTWLm0J50/z+\nFPCsqsZ5jusDf1PVyW4HZ4wvfbIhlpmLt7IvLpnmocE8NKQj14S3YMeOU6eZHT7sXN+uHdxwg1MT\n798fmjTxafimLHm22SQ9GZoB8XtJXDiOvQkx7Gl2foHTu7xtFs+bsMPqhdlocXNGvPm6d7mqPpJ9\noKrHReQKwJK68VufbIhl4kebSU7PJCOxBlt/asAf/6tUP5TB4QPOP5vmzWHo0NxpZq1a+Tho45qE\nJY8TnZ7A12Twv9+f4wgJxGUCS/+Wc03eZvGI5hFcd951OQnbmsVNefEmqQeKSA1VTQUQkWCghrth\nGeNbz/5vK0d/akzcqnakH3LazQNqphHa7hhzHmvCwIHOPuP2/7N/ysjKYE3sGr7e8TVf7/ya7xJ+\nJVMgWOGCwNoMybPlZqvbv7ZmcVNhePM38B1gqYi84Tm+DdutzfgpVfjqK1j/YjfSDoRSrUES9Qds\noebZRwlqkkCAwD33XOnrME0ZU1W2HduWk8SjdkeRkJqAIHRv3p2/12hEZOpJLiaQ1S3vof/Wx503\nhoSBm2uLG1NC3gyUe0ZEfgQGe05NU9XF7oZlTPlbsQImTXJ+1gitQcMrfqT2BbFIQO5ApuahNmTd\nXxw5eYSlO5fy9U4nke+J3wNA69DWjL5gNJFtIhl4zkAa1mp4ap96tgqwzaYx+XkzUO4cIFpV/+c5\nDhaR1qq62+3gjCkP69c7yXzxYmjWDGbPhiYRx3js8/0kp+cm9OCgQB4a0tGHkZozkZKRwso9K3OS\n+Ib9G1CUkBohDDxnIBP6TCCybSRt67c9vd877zab4NTQK8A2m8bk503z+wLg4jzHmZ5zF7kSkTHl\n5Oef4bHH4KOPoEEDePZZuPdeqFULoAVB1Tlt9Pvwri18HbbxUpZmsfng5pwk/s3v35CSkUK1gGr0\nbtmbJ/o/QWTbSCKaR3jXF569zWZ0NNzwk+vxG1Ma3iT1aqqaln2gqmkiUt3FmIxx1Y4dMGUKvPOO\nszzr44/DX/96+jzy4V1bWBKvZGITYnOS+JKdSzh04hAA5zU6j7HdxhLZNpJ+Z/ejbo26Po7UGHd4\nk9QPi8gwVV0IICLXAEe8ubmIDAVeAAKBuao6o5DrRgAfABep6jqvIjemhGJjnWVZX38dqlWDBx90\nVnhr1MjXkZnSSkxNZPnvy3MGuP1y5BcAmtRuwuA2g4lsE8ngNoNpWa+ljyM1pnx4k9TvBt4RkdmA\nAHuBPxT3JhEJBF4CIoEYYK2ILFTVLfmuqwv8Bfi+hLEb45XDh2HGDHjpJWcFuLFjnT705s19HZkp\nqYysDNbtW5eTxFfHrCYjK4Oa1WrS9+y+3N71diLbRNKpaScCxHbEMVWPN6PfdwC9RKSO5zhJRJp6\nce8ewHZV3QkgIvOBa4At+a6bBjwDPFSSwI0pTlwcPP88zJoFJ0/CH/7gNLW3bu3ryIy3VJUdx3fk\nJPFlu5YRnxqPIHQ9qyt/6/03IttE0qdVH2pWq+nrcI3xuZKslFANGCEiNwLnAcXVc1rg1OqzxQA9\n817g2QEuTFW/EBFL6qZMnDgBL74IM2fC8eMwciRMnQrnnuvryIw3jiUfO2Wq2e643QC0CmnF9edf\nT2SbSAa1GUSjWtZvYkx+oqqFv+isHncNcCPQFagLDAe+UdWsIm8scj0wVFXv9BzfAvRU1XGe4wBg\nGfBHVd0tItHAgwX1qYvIWGAsQNOmTbvPnz+/pOX0WlJSEnXq1HHt/r5QVcqUliZ89llz3nnnbI4f\nr06vXke5/fZdtG+f5KMoS6aq/J7yS8tK4+f4n1l3fB3r49bzW+JvKErtwNqEh4bTvX53IupH0DK4\nZYVYYtUff0/gn+XylzINGDBgvapGeHWxqhb4AN7FqWm/jtMvHgjsKuz6At7fG1ic53giMDHPcQjO\ngLvdnkcKsA+IKOq+3bt3VzdFRUW5en9f8PcypaervvaaaliYKqj276+6cqXvYistf/89ZcvKytJN\nBzbp86ue16FvD9Va02spU9DAJwK1z+t9dErUFF25Z6WmZ6aXf8Be8Mffk6p/lstfygSsUy9zb1HN\n7+cDx4FfgF9UNVNECq/Wn24t0N6zeE0sMAanxp/9ZSIeyGk/K6qmbkxBsrLgvfecuebbt0OPHjBv\nnrPVaQWo0Jk89iXuY8nOJTlTzQ4kHQCgY8OO3B5+O5FtI+nfuj/1atTzcaTGVG6FJnVVDReRc4Eb\ngCUicgSoKyJNVfVgcTdW1QwRGQcsxqnlz1PVn0VkKs63joVlVAZTxajCt9825P77YfNm6NQJPv0U\nrr7aknlFcSLtBMt/X84b299g3JZx/Hz4ZwAa1Wp0ylSzViG2tZ0xZanIgXKq+ivwOPC4iHTHSfBr\nRSRGVS8u6r2e9y8CFuU7V+Biyara39ugTdWk6uxhPmkSrFnTifbt4d13YfRoCLDZSz6VmZXJ+v3r\nc0apr9q7ivSsdIIkiH7n9OMPXf5AZJtIujTrYlPNjHGR16PfVXU9sN4zSv1S90Iy5nSrVjnJPDoa\nwsLgwQd/5emnz6Wa7XTpMzuP7zxlqtnxlOMAhDcLZ3yv8US2iSRrdxZDBg3xcaTGVB0l/i/R02n/\njQuxGHOajRth8mT44gto0gReeAH+9CdYvfoA1arZHLXydDz5OMt2LcuZarbz+E4AWtZryfBzh+dM\nNWtSu0nOe6L3RvsoWmOqJqvnmArp11+dAXALFkD9+vD003DffVC7tq8jqzrSMtNYvXd1ThJft28d\nWZpFnep1GNB6AON7jieybSQdG3asEFPNjDHebb0aqKqZ5RGMMbt2wRNPwFtvObulTZ4Mf/sbhIb6\nOjL/p6psObwlJ4kv372cE+knCJRAerToweRLJxPZNpKeLXoSFBjk63CNMQXwpqa+TUQ+BN7QfOu2\nG1NW9u2D6dPhtdecQW/jx8OECdC4sa8j828Hkg6cMtVsX+I+ANo3aM+tXW4lsm0kA1oPIKRmSDF3\nMsZUBN4k9S44c8znelaBmwfMV9UEVyMzVcKRI/DMMzB7NmRkwJ13OrXzFrbjqStOpp/km9+/yRng\ntvnQZgDyzBrwAAAgAElEQVQaBjdkUJtBRLaJJLJNJGeHnu3jSI0xpeHNhi6JwGvAayLSD2eluX+K\nyAfANFXd7nKMxg8lJMA//uE8kpLg5pudzVbatvV1ZP4lS7P4Yf8POUl85d6VpGWmUT2wOpe0uoSn\nBz1NZJtIup7V1aaaGeMHvOpTB64EbgNaA88D7+BMa1sEdHAxPuNnTp50auXPPAPHjsF11zmbrVxw\nga8j8x+743bnJPGlu5ZyLPkYAJ2bdua+HvcR2SaSS8++lFpBtXwcqTGmrHnVpw5EATNVdVWe8x+I\nSF93wjL+Ji3N6S9/8kk4cACGDnWed+/u68gqv7iUOKJ2ReUMcNt+zGk8a163OVd3uDpnqlmzOs18\nHKkxxm3eJPXOqlrgNleqen8Zx2P8TEaGM5L9iSfg99/h0kvh/fedn6Z00jPT+S7mu5wkviZ2DVma\nRe2g2vRv3Z9xF40jsm0k5zU6z6aaGVPFeJPUm4jIf3F2XcsCVgN/VdWdrkZmKrWsLPjgA2eu+dat\nTo38lVfgsstsffaSUlV+PfJrThKP3h1NUloSARLARc0v4pFLHiGybSS9WvaiemB1X4drjPEhb5L6\nu8BLwLWe4zHAf4GebgVlKi9VZ/W3yZPhxx+dvvKPPoLhwy2Zl8ShE4dOmWoWkxADQNv6bbm50805\nU83qB9f3caTGmIrEm6ReS1XfynP8tmf9d2NOERXlrM++ejW0aQNvvw1jxkBgoK8jq/iS05NZe2wt\nX3z1BV/v/JofD/4IQP2a9U+ZanZO/XN8HKkxpiLzJql/KSITgPmAAqOBRSLSAEBVj7kYn6kEvv/e\nSeZLlzrzy195BW67DYJs0bFCZWkWGw9szBml/u2eb0nNTCUoIIg+rfowfeB0IttE0u2sbgQG2Lci\nY4x3vEnqozw//5Tv/BicJN+mTCMylcamTU4z+2efOSu//fOfcPfdULOmryOrmPbE7zllqtmRk0cA\nuLDJhfz5oj/TNKkp464eR+3qtsC9MaZ0vFl8xtr7zCl++81ZKGb+fAgJcaam/eUvUKeOryOrWBJS\nE06Zavbb0d8AaFanGZe3u5zBbQYzuM1gmtdtDkB0dLQldGPMGfFm8Zkg4B4ge056NPCKqqa7GJep\ngPbscRaK+c9/oEYNmDgRHnrI2UXNQEZWBmti1/D1jq/5audXfB/zPZmaSa2gWvQ7ux93d7+byLaR\nXND4AptqZoxxhTfN7/8GgoA5nuNbPOfudCsoU7EcOABPPeX0lQOMG+ck9KZNfRuXr6kq245ty2lS\nj9odRUJqAoIQ0TyCh/s8TGTbSHq37E2NajV8Ha4xpgrwJqlfpKpd8hwvE5Ef3QrIVBzHjsGzz8K/\n/gWpqXD77fDooxAW5uvIysmm92HpVIiPgZCWMOgxjrQbyNKdS3Oa1PfE7wGgdWhrxlwwhsi2kQw8\nZyANghv4OHhjTFXkTVLPFJG2qroDQETaALa/uh9LTIRZs+C555znN9wAU6ZA+/a+jqwcbXofPruf\nlPSTrCSTr+O38fXHN/EDGQCE1Ahh4DkDmdBnApFtI2lbv601qRtjfM6bpP4QECUiOwEBzsbZ3MX4\nmeRkmDMHZsxwtkQdPtzpQ+/UydeRlb+UJVN4KT2Op0jjmCjVFC7WQKbVbMLgmz8lonkE1QK8+edj\njDHlp8j/lTz7pycD7YGOntNbVTXV7cBM+UlLg3nzYNo02LcPIiOdEe09evg6svKXmZXJ25ve5tGE\nX9kryhANZJxWpx/VqItASiq07OXrMI0xpkBFJnVVzRKRl1S1K7CpnGIy5SQzE955x2la37UL+vSB\nd9+Ffv18HVn5U1UWbVvEhKUT+OnQT3QPrMkbmQEMyv9PJKSlbwI0xhgvBHhxzVIRGSHWYeg3VOHD\nD51m9VtvhdBQZ732FSuqZkL/PuZ7Brw5gKv+exXJ6cm8d/17rBn2BoOC6p56YVAwDHrMN0EaY4wX\nvOkU/BPwAJAhIik4/eqqqvVcjcycsU82xDJz8VbGhCUyacYyHrysIzUPtmDyZPjhBzj3XFiwAK67\nDgK8+XrnZ7Ye2cqkZZP48JcPaVK7CbMvn81d3e/K3elMAk4b/U7nUUXf1BhjfMibFeXqFneNqXg+\n2RDLxI82k5yeCWGwY3MwN80JJnkvtG7tLCBz881Vc7OV/Yn7eWL5E8z9YS7BQcFM6TeFB3o/QN0a\n+f6qdx5lSdwYU6l4s6LcUlUdVNw5U7HMXLyV5PRM0o/UYc4z53FwcxMC66TQ5pqt/PJ+R6pXwW23\nE1ITmLlyJv/47h+kZaZxT8Q9TO47maZ1qvgqOsYYv1FoUheRmkAtoJGI1MdpdgeoB7Qoh9jMGdgX\nl0zy7w05/FF34qor9QdsoU7X38kKyqJ69Y7F38CPpGak8vK6l3lyxZMcOXmE0ReM5smBT9KuQTtf\nh2aMMWWqqJr6n4DxQHNgPblJPQGY7XJc5gwF7WrNoQ/OI6jBCf4+eTVv7HeW6m8eGuzjyMpPlmbx\n383/5dGoR9kVt4uB5wzkmcHPENE8wtehGWOMKwpN6qr6AvCCiNynqv8qx5jMGVB1Fo/Z9v4F1Dr7\nKA2Hr6NBoxTYX43goEAeGuL/tXRV5eudX/PwkofZeGAj4c3CWXzzYiLbRNqqb8YYv+bNQLl/icjF\nQOu816vq/7kYlymFjAxns5VXXoEbb4Th96cwKyoISKFFaDAPDenI8K7+3XOyft96Hl7yMEt3LaV1\naGveue4dxlw4hgCpgsP7jTFVjjcD5d4C2gIbyV3zXQFL6hXIiRMwZgx8/jlMmADTp0NAQAtG9mxB\ndHQ0993U39chumrHsR1MWjaJ935+j4bBDZk1ZBZ3R9xtu6MZY6oUb+apRwDnq6q6HYwpnYMH4aqr\nnLnnc+bAPff4OqLyc+jEIaYtn8bL61+memB1Jl86mQcvfpCQmiG+Ds0YY8qdN0n9J6AZsN/lWEwp\nbN0Kl1/u7Hn+8ccwbJivIyofJzNO8kT0Ezy3+jmS05O5q9tdPNbvMc6qe5avQzPGGJ/xJqk3AraI\nyBogZyMXVa0i6aPiWrUKrr7aWUAmOrpqbMCSnpnOaz+8xuQ1kzmefpwR541g+sDpdGzk/wMAjTGm\nON4k9SluB2FK7qOP4KabICwMvvwS2rb1dUTuUlUWbFnApGWT2H5sO51DOrPo+kX0sh3TjDEmR1GL\nz5yrqr+q6nIRqZF3u1UR8ep/UhEZCrwABAJzVXVGvtfvBu7FGYCXBIxV1S2lKEeV8sIL8Ne/Qq9e\nsHAhNGrk64jctWzXMh5e8jDr9q3jwiYX8sWNXxAcE2wJ3Rhj8ilqns+7eZ6vzvfanOJuLCKBwEvA\n5cD5wA0icn7+z1DVTqoaDjwL/KP4kKuurCx44AEYPx6GD4elS/07of944EeGvj2UQf83iINJB/nP\nNf9h4582ckX7K2y+uTHGFKCo5ncp5HlBxwXpAWxX1Z0AIjIfuAbIqYmrakKe62vjTJUzBUhJgT/8\nwdlV7f774R//8N/NWHbH7ebRqEd5Z9M7hNYM5bnI57i3x73UrFbT16EZY0yFVlRS10KeF3RckBbA\n3jzHMUDP/BeJyL04W7tWBwZ6cd8q59gxuOYa+PZbeP55p+ndHyuqR04eYfo305mzbg4BEsDDfR7m\n4UseJrRmqK9DM8aYSkEKm34uIoeA+Ti18tGe53iOR6lqkVtbicj1wFBVvdNzfAvQU1XHFXL9jcAQ\nVb21gNfGAmMBmjZt2n3+/Pn5LykzSUlJ1KlTx7X7l9T+/TV5+OHOHDhQk4kTf2HAgMMlvkdFK1N+\nyZnJfBjzIfP3zic5M5mhzYbyx9Z/pHGNxoW+p6KXqTSsTJWDP5YJ/LNc/lKmAQMGrFdV7zatUNUC\nH8CtRT0Ke1+e9/cGFuc5nghMLOL6ACC+uPt2795d3RQVFeXq/Uti7VrVpk1V69dX/eab0t+nIpUp\nr/TMdH1l3St61nNnKVPQa/57jf586Gev3ltRy3QmrEyVgz+WSdU/y+UvZQLWaTG5MftR1IYub5bu\nO0WOtUB7ETkHiAXGADfmvUBE2qvqNs/hlcA2DACLFsHIkdC4MURFwXnn+TqisqOqfPzrxzyy9BG2\nHt3KxWEXs2DkAvq06uPr0IwxplLzZp56qahqhoiMAxbjTGmbp6o/i8hUnG8dC4FxIjIYSAeO47QC\nVHmvveYs9dqlC3zxBTRr5uuIys6K31fw9yV/57uY7ziv0Xl8OuZTru5wtY1mN8aYMuBaUgdQ1UXA\nonznHsvz/C9ufn5lowqPPupsxnL55fD+++AH3UEA/HToJyYuncjnv31O87rNmXv1XG4Nv5VqAa7+\nFTTGmCrF/ketINLS4M474a23nJ///jdU84Pfzp74PTwe/ThvbnyTejXqMWPQDO7reR+1gmr5OjRj\njPE73my9+izwJJAM/A/oDPxVVd92ObYqIz4eRoxwFpOZNg0mTar8U9aOJR9jxrczePH7F1GUB3o/\nwCOXPkKD4Aa+Ds0YY/yWN3XBy1T17yJyLbAbuA74BrCkXgZiYuCKK+CXX+DNN50FZiqz5PRk/rXm\nXzz97dPEp8Tzhy5/4In+T3B26Nm+Ds0YY/yeN0k9+5orgQWqGm+Dms7MJxtimbl4K7u3VePIBz0I\nzKzOokUBREb6OrLSy8zK5M0f3+SxqMeITYzlyvZX8vSgp+nUtJOvQzPGmCrDm6T+uYj8itP8fo+I\nNAZS3A3Lf32yIZaJH23m2LZQDn/cnYDqmTS/cTUnGrXGWYSvclFVPv/tcyYsncCWw1vo2aIn71z3\nDv1a9/N1aMYYU+UUtaELAKo6AbgYiFDVdOAEzhruphRmLt5K4tEgDn1wEdXqJdPslpVogzhmLt7q\n69BKbNXeVfT9T1+GzR9GRlYGH4z8gNV3rLaEbowxPlJsUheRkUC6qmaKyGScvvTmrkfmp/bFJXNy\nazPIDKTxtT9QrV5KzvnK4pfDv3Dte9fSZ14fth/bzstXvsxP9/zEiPNH2HxzY4zxIW+a3x9V1QUi\ncgkwGJgJ/JsCNmcxxWseGsz+rWcR1CSeoAYnTjlf0cUmxDIlegrzNs6jdlBtnhzwJON7jad29dq+\nDs0YYwzeJfVMz88rgVdV9QsRedLFmPzabeHnsSq2AaGX5ja3BwcF8tCQjj6MqmhxKXE8u/JZZn03\ni4ysDO7rcR+TLp1E49qFb7hijDGm/HmT1GNF5BUgEnhGRGrgRbO9KdjJ384C4JyLjnMcp4b+0JCO\nDO9a8QbJpWak8tLal5i+YjrHko9xU6ebmDZgGufUP8fXoRljjCmAN0l9FDAUeE5V40TkLOAhd8Py\nXwsWQKdO8MPzvXwdSqEyszJ5d/O7PBr1KL/H/86QtkN4etDTdD2rq69DM8YYU4Rik7qqnhSRHcAQ\nERkCrFDVr9wPzf/s2wcrV8ITT/g6koKpKv/b/j8mLJ3ApoOb6H5Wd14f9jqD2gzydWjGGGO84M3o\n978A7wBNPI+3ReQ+twPzRx9+6GzaMnKkryM53drYtQz8v4Fc8e4VnEg7wfwR81lz1xpL6MYYU4l4\n0/x+B9BTVU8AiMgzwGrgX24G5o8++AAuuADOPdfXkeTadnQbk5ZNYsGWBTSu1ZjZl8/mru53UT2w\nuq9DM8YYU0LeJHUhdwQ8nuc2GbmE9u+HFSvg8cd9HYnjQNIBpi6fyms/vEaNwBo83u9x/tb7b9St\nUdfXoRljjCklb5L6G8D3IvKx53g48Lp7Ifmnjz6qGE3vCakJPLfqOZ5f/TxpmWn8qfufeLTvozSt\n09S3gRljjDlj3gyU+4eIRAOXeE7dpqobXI3KDy1YAOef7zzKzab3YelUaHYnaf/4M6+c05Np27/g\n8MnDjL5gNE8OfJJ2DdqVY0DGGGPcVGRSF5FA4GdVPRf4oXxC8j8HD8I338Cjj5bjh256Hz67n6z0\nkyyt9QN3JPzKzk2/MLDxhTxz0yIimkeUYzDGGGPKQ5FJ3bPe+1YRaaWqe8orKH/jk6b3pVNJSz/J\nGJL5+MBbdCGA/2kwl6VkIZbQjTHGL3nTp14f+FlE1uDs0AaAqg5zLSo/s2CBM+L9ggvK7zPT4vcy\nmmQ+kQzuaTSM2YejCEAgIbb8gjDGGFOuvNrQxfUo/NihQ7B8OTzyCJTXBmZpmWmMClI+zchgttbk\nggYDCDgc7bwY0rJ8gjDGGFPuCk3qItIOaKqqy/OdvwTY73Zg/uLjjyErq/ya3tMy0xi1YBSfZiQy\nO6Au92YK0dkvBgXDoMfKJxBjjDHlrqgV5WYBCQWcj/e8ZrywYAF06OCs9+62nIS+9VNmXz6be6+Z\nCyFhzoshYXD1i9B5lPuBGGOM8Ymimt+bqurm/CdVdbOItHYtIj9y+DBERcHEie43vadlpjFywUgW\nbl3IS1e8xJ8v+rPzQudREB0NN/zkbgDGGGN8rqiaemgRrwWXdSD+KLvp/frr3f2cQhO6McaYKqWo\npL5ORO7Kf1JE7gTWuxeS//jgA2jXDrp0ce8z0jLTuP7961m4dSFzrphjCd0YY6qwoprfxwMfi8hN\n5CbxCKA6cK3bgVV2R47AsmXw97+71/SempHKyAUj+ey3z5hzxRzuuegedz7IGGNMpVBoUlfVg8DF\nIjIAuNBz+gtVXVYukVVyn3wCmZnujXq3hG6MMSY/b9Z+jwKiyiEWv7JgAbRpA+HhZX/v1IxUrl9w\nPZ//9jn/vvLf3B1xd9l/iDHGmEqnqD51U0pHj8LSpU4tvayb3i2hG2OMKYw3K8qZEvr0U3ea3lMz\nUhnx/gi+2PYFL1/5Mn+K+FPZfoAxxphKzWrqLliwAFq3hm7dyu6eltCNMcYUx5J6GTt2DJYsKdum\n97wJ/ZWrXrGEbowxpkDW/F7GFi6EjIyya3pPzUjluvevY9G2Rbxy1SuM7T62bG5sjDHG71hNvYwt\nWABnnw0RZbBleUpGiiV0Y4wxXrOkXobi4uDrr51lYc+06T0lI4UR749g0bZFvHrVq5bQjTHGFMvV\n5ncRGQq8AAQCc1V1Rr7XHwDuBDKAw8Dtqvq7mzG56dNPIT39zJveUzJSuO696/hy+5e8etWr3NX9\ntNV6jTFVQHp6OjExMaSkpLj+WSEhIfzyyy+uf055qmxlqlmzJi1btiQoKKjU93AtqYtIIPASEAnE\nAGtFZKGqbslz2QYgQlVPisg9wLPAaLdictsHH0CrVtCjR+nvkTehv3b1a9zZ7c6yC9AYU6nExMRQ\nt25dWrdujbi81WNiYiJ169Z19TPKW2Uqk6py9OhRYmJiOOecc0p9Hzeb33sA21V1p6qmAfOBa/Je\noKpRqnrSc/gd0NLFeFwVHw9ffXVmTe8pGSlc+961ltCNMQCkpKTQsGFD1xO68T0RoWHDhmfcKuNm\nUm8B7M1zHOM5V5g7gC9djMdVCxdCWlrpt1nNTuj/2/4/S+jGmByW0KuOsvhdV4gpbSJyM84OcP0K\neX0sMBagadOmREdHuxZLUlJSqe7/8ssX0rhxHZKTv6Okb0/LSuPRnx5lzfE1PNjhQdoltCvTMpa2\nTBWZlalysDKdmZCQEBITE8vlszIzM0/7rKNHjzJs2DAADh48SGBgII0aNQIgKiqK6tWre3Xvt956\ni8suu4ymTZsCcM899/DAAw/Qvn37MizB6QoqU0WXkpJyZn+/VNWVB9AbWJzneCIwsYDrBgO/AE28\nuW/37t3VTVFRUSV+T3y8avXqquPHl/zzktOTdchbQ1SmiM5dP7fkN/BCacpU0VmZKgcr05nZsmVL\nuX1WQkJCka8//vjjOnPmzFLdu0+fPrphw4ZSvfdMFFemiqig3zmwTr3MvW42v68F2ovIOSJSHRgD\nLMx7gYh0BV4BhqnqIRdjcdVnnzlN716Pet/0PvzzQlKmhDB8RmO+2vEVc4fN5Y5ud7gapzHGlJU3\n33yTHj16EB4ezp///GeysrLIyMjglltuoVOnTlx44YW8+OKLvPfee2zcuJHRo0cTHh5OWloal1xy\nCRs3biQjI4PQ0FAmTJhAly5d6N27N4cOOalg27Zt9OzZk06dOjFp0iRCQ0N9XOLKwbXmd1XNEJFx\nwGKcKW3zVPVnEZmK861jITATqAMs8PQl7FHVYW7FVNY+2RDLzMVb2TDvfKrXC+VAjaMUPWwAJ6F/\ndj8p6ScZzkm+yshkbmA9bg+sUy4xG2Mqp/HjYePGsr1neDjMmlXy9/300098/PHHrFq1imrVqjF2\n7Fjmz59P27ZtOXLkCJs3bwYgLi6O0NBQ/vWvfzF79mzCC9iLOj4+nn79+jFjxgweeOAB5s2bx4QJ\nE7jvvvt48MEHGTlyJLNnzz7TolYZri4+o6qLVLWDqrZV1emec495EjqqOlhVm6pquOdRqRL6xI82\ns/dgGsk7G1Oj/X4mfbKZTzbEFv3GpVPJyE7oZDKXmtye6Zw3xpjKYMmSJaxdu5aIiAjCw8NZvnw5\nO3bsoF27dmzdupX777+fxYsXExISUuy9goODufzyywHo3r07u3fvBuD7779nxIgRANx4442ulcXf\nVIiBcpXRzMVbSU7PJHnnWZAZSK2O+0lOz2Tm4q0M71pEbT0+hndIZ7FkMkdrcjvVc84bY0xhSlOj\ndouqcvvttzNt2rTTXtu0aRNffvklL730Eh9++CGvvvpqkffKO9guMDCQjIyMMo+3KrFlYktpX1wy\nACm7GxFQI50aLY6fcr4wGfVaMI1UumoAd5Nn1aCQSjtF3xhTxQwePJj333+fI0eOAM4o+T179nD4\n8GFUlZEjRzJ16lR++OEHAOrWrVviUeg9evTg448/BmD+/PllWwA/ZjX1UmoeGkxsXDIpvzeiRquj\nSEDu+aK81b4vO37YwkKtieCZkxgUDIMeczliY4wpG506deLxxx9n8ODBZGVlERQUxMsvv0xgYCB3\n3HEHqoqI8MwzzwBw2223ceeddxIcHMyaNWu8+owXX3yRW265hSeeeIIhQ4Z41ZRvLKmX2kNDOvK3\n17eTEV+LuhftAiA4KJCHhnQs9D3pmelM27mYiNA2XJVZExJinRr6oMeg86jyCt0YY0psypQppxzf\neOONBfZ1b9iw4bRzo0aNYtSo3P/jvv3225zncXFxOc/HjBnDmDFjAGjZsiXff/89IsLbb7/Nzp07\nz7QIVYIl9VIa3rUFXzeoxRwg+OwjtAgN5qEhHYvsT3/zxzfZFbeL2Td+gbS/ovyCNcaYSmbt2rWM\nHz+erKws6tevzxtvvOHrkCoFS+pn4Pj2+jRrBjGv9St2vfe0zDSmfTONni16cnm7y8snQGOMqaT6\n9+/PxrKew1cFWFIvJVVYtgwGDfJuA5c3NrzBnvg9vHLVK7aWszHGGFfY6PdS2rIFDh50knpxUjNS\nmb5iOr1b9mZI2yHuB2eMMaZKspp6KS1d6vwcOLD4a1/f8Dp7E/by+rDXrZZujDHGNVZTL6Vly6BN\nG2jduujrUjJSeGrFU/QJ68PgNoPLJTZjjDFVkyX1UsjIgOho72rpc3+YS2xiLFMHTLVaujGmUjl6\n9Cjh4eGEh4fTrFkzWrRokXOclpbm9X3mzZvHgQMHco5vu+02tm7dWubxtmzZ8pQpcmUpe/MZgL17\n9zJ69GhXPudMWfN7KWzYAPHxxfenJ6cn89SKp+h7dl8GtB5QPsEZY0wZadiwYc4I9ClTplCnTh0e\nfPDBEt9n3rx5dOvWjWbNmgFU+ulpYWFhvPfee74Oo0BWUy+F7P70AcXk6VfXv8r+pP1M7W+1dGOM\nf6moW68+/fTTdOrUiZ49e7Jrl7Mw2KeffkrPnj3p2rUrl112Wc5nLFu2jC5duhAeHk63bt04ceIE\nADNmzKBHjx507tyZqVNP32xr+/btOTvOzZ07l+uvv54hQ4bQvn17Jk6cmHPdl19+Se/evenWrRuj\nR4/Oub+brKZeCsuWwYUXQtOmhV9zMv0kT3/7NANaD6Bf637lF5wxxi/Z1quOzMxMevbsybp16wqM\nrUGDBmzevJl58+bxyCOP8Pnnn9O3b1+GDRuGiPDyyy/z/PPP88wzzzBz5kxeffVVevbsSVJSEjVr\n1mTRokXs2bOH77//HlXliiuuYNWqVfTo0aPQP48ff/yR9evXExQURIcOHbjvvvuoVq0aM2bMYOnS\npdSqVYvp06fzwgsv8Mgjj5T8D7wELKmXUGoqfPst3HVX0de9vO5lDp44yIKRC8onMGOMKSd5t14F\nSE5OJiwsjCFDhuRsvXrllVdy2WWXFXuv/FuvrlixAnC2Xl20aBHgLEk7efJkwNnJrbCEDnDDDTcA\ncNNNN/Hwww8DsGfPHkaNGsWBAwdITU2lQ4cOAPTp04e//OUv3HTTTYwYMYI6derw1Vdf8eWXX9K1\na1cAkpKS+O2334pM6oMHD6ZevXoAnHvuuezZs4cDBw6wZcsWLr74YoCcFgq3WVIvoe++g+TkovvT\nT6Sd4JmVzzC4zWAuPfvS8gvOGOO3bOtV7xTU1XnvvffyyCOPcMUVV7BkyRJmzJgBwOTJkxk2bBhf\nfPEFvXr1YunSpagqkydP5o477jjlHkXFVaNGjdPKoKoMHTqUt95664zKU1LWp15CS5dCQAD07Vv4\nNXPWzuHQiUM80f+J8gvMGGPKSUXeejV7ANt///tfevXqBThN/C1atEBVefPNN3Ou3bFjB507d2bi\nxIl069aNrVu3MmTIEF5//fWc/u+YmJiccpbExRdfzPLly3M2ojlx4gTbtm0r8X1KymrqJbRsGURE\nQGFjNpLSknh21bNc1vYyLg67uHyDM8aYcuDLrVeL61M/cuQInTt3Jjg4mNdeew1wRu5fe+21NGjQ\ngP79+7N//34AnnvuOVasWEFAQACdO3fmsssuo3r16vz66685Xwjq1q3Lu+++6/VAvWxNmzbl9ddf\nZ/To0TnT/5566inat29fovuUmKpWqkf37t3VTVFRUYW+lpioWq2a6oQJhb9/xooZyhR09d7VZR9c\nKQR+iCMAABQvSURBVBVVpsrKylQ5WJnOzJYtW8rtsxISEsrts7yRlJSkWVlZqqr61ltv6XXXXVfi\ne1S0MnmjoN85sE69zJFWUy+BFSuchWcK609PTP3/9u49uorqeuD4dxOC4REgiAswSRV5aQKEhGDL\nQwtKeWMQKKRLRZGfVCpWf+pPbGspdaFSiwpY28oSqQJqwVfpAxEDsSKL8FATLAESEBsQBaOJRIkh\nZP/+mEm4edwkJLm5D/Znrbsy98zcmbPnJPdkZs7MPslj2x5jbM+x/CDmB81bOWOMCSGWerVhrFM/\nB2lp0KoVDPFyVv2pHU/x5akv7Vq6McY0kqVebRgbKHcONm92OvQ2barPKywuZPG2xUzoPYFB0YOa\nv3LGGGPOe9ap11N+vvPgB2/Pe1+WsYyvir9iwQ8XNGu9jDHGmHJ2+r2e0tNBtcr19Ky1kPYQBYV5\nPCFFpHRLZuDFA/1VRWOMMec5O1Kvp7Q0aNcOBpWfWc9aC3//ORTmsYRiCihjwfFDTrkxxhjjB9ap\n19Pmzc4DZ8LD3YK0h+D0Kb5CeZISrteWDCgtdcqNMSYENEXq1fqkWX366adZs2ZNU1T5vGen3+vh\n6FHYv7/K894Lj6Aod1PMSWABF1SUG2NMKKhP6tXy+6NbtKj5GLE+t6Ldcccdja+sAexIvV42b3Z+\nVrqe3iGGFZzmBTnNb7iA/oRVlBtjTCjLzc0lLi6OG264gfj4eI4dO8bs2bNJTk4mPj6+UrrS+qRZ\nffDBB1niPtx+2LBhPPDAA1x55ZX06dOHbdu2Ac5jVqdMmUJcXBxTp04lOTnZbnmrgR2p10NaGlx4\nIfTvf7bsw4EzmLtlHj/SMB7ETUgQ3hqune+fShpjQtrdb97Nh581bSc2oOsAloxpWKaYffv28cIL\nL1Rkalu0aBGdOnWitLSUESNGMHXqVOLi4ip9xlua1apUlR07drB+/Xoeeugh3nzzTZ566im6du3K\nq6++SmZmJklJSQ2qd6izI/U6qDpH6iNGOIlcwLknfeqHy+kc0Yk1kT0JowV0iIWJy6D/NP9W2Bhj\nmkGPHj0qOnRwEqgkJSWRlJREdnY2e/furfaZqmlWDx8+XOO6J0+eXG2ZrVu3kpqaCkBCQgLx8fFN\nGE3osCP1OuTmQl4elOe1V1VmrZ/F4YLDpN+SzkXf831+XGOMaegRta+0bdu2YjonJ4elS5eyY8cO\nOnbsyI033khxcXG1z9Q3zWp5KtOmSMV6vrEj9TqUX08vf+jMsoxlvJr9KotGLmKYdejGGMPXX39N\nZGQk7du359ixY2zcuLHJtzF06FDWrnVuGd6zZ0+NZwKMHanXKS0NYmKgVy/YfmQ79226j5Q+Kdw7\n+F5/V80YYwJCUlIScXFxXH755VxyySUMHTq0ybdx5513MmPGDOLi4ipe5elYzVnWqdeirAy2bIFx\n4+DLU/lMWzeNmPYxrExZiYj4u3rGGNNsFixYUDHds2fPSiPPRYRVq1bV+LmtW7dWTBcUFFRMp6am\nVlwjX7hwYY3Ld+3aldzcXAAiIiJ48cUXiYiIICcnh1GjRhEbG9u4oEKQdeq12LMHvvgCRlxTxk2v\n38Tn33zOtlu3EdU6yt9VM8aY80pRURHXXnstpaWlqCrPPPMMLVtaF1aV7ZFalF9Pz75wERt2b+CP\n4/5oz3Y3xhg/6NixI7t37/Z3NQKeTwfKicgYEdkvIrkiUu1mRBG5WkTeF5FSEZnqy7rUKWstPNkX\njn3o/Mxa61xPH7aFxe//mtS+qdyefLtfq2iMMcbUxmeduoiEAU8DY4E44CciEldlsf8CtwAv+qoe\n9ZK1ltK/3QmFec77wjxK1s1hy7Yc8q+6nl5tu7J8wnK7jm6MMSag+fJI/UogV1UPqWoJ8DKQ4rmA\nqh5W1SygzIf1qNO3G+bT8kwxRSh/L9iGomR8msC3KbdxJryIV059R+T+Df6sojHGGFMnX15Tjwby\nPN4fAb7vw+01WMSpzwC474tLeEbWcaqsNTtbnoSLd/H4d53pKyVO9jV7WpwxxpgAFhQD5URkNjAb\noEuXLqSnpzfp+k/3Xki4lHJZSQzseps/JT8DF++i/f6b6DsxiYqtNfF2m0tRUVGT7zN/s5iCg8XU\nOB06dODkyZPNsq0zZ85U21Z+fj7XXXcdAJ9//jlhYWF07twZgC1btlR6Qpw3c+bM4Z577qFXr15e\nl1m+fDkdOnRg+vTpjYigulmzZnH99dczYcKEJl1vuVGjRrF48WL69+/PpEmTWLVqFZGRkY1aZ3Fx\ncaN+v3zZqR8FPG8ijHHLzpmqLgeWAyQnJ+vw4cMbXTlPCxZuYf7pJVwdLjyRcZhW2dfzacH3uK33\nWwzf7yRo+YyL6PqT3CbdbnNJT0+nqfeZv1lMwcFiapzs7OxGdxL1dfLkyWrbioyMJCsrC2h46tXV\nq1fXue177/XNw7xEhNatW/tsH4aFhdG2bVsiIyNJS0trknVGRESQmJjY4M/78pr6TqCXiHQXkVZA\nKrDeh9trsAHjZ7P6zEhAGT98L3kHR/P9NoX88qonAfhWW/FoyY/9W0ljjAkQwZR6dePGjQwcOJDe\nvXuzYYMzNurgwYNcddVVJCYmMnDgQDIyMgA4evQow4YNY8CAAfTt27di2xs2bGDw4MEkJSUxffp0\nvvnmm2rbiYmJoaCggNzcXPr27cusWbOIj49n7NixFc/Bz8nJYfTo0QwcOJCrr76aAwcONLQJvPLZ\nkbqqlorIXGAjEAY8p6r/EZGHgF2qul5EBgGvA1HARBH5rao2e+qdSYnRXPrXW9lV1puUMVkMLl7D\n9L6v0S78W46Udeax0mnsbv+j5q6WMcZUsNSr3lOvzpw5k7vuuosBAwZUW1deXh47d+4kJyeHkSNH\nkpubS7du3di0aRMRERHs27ePm2++mYyMDFavXs3EiROZN28eZ86c4dSpUxw/fpxFixaRlpZGmzZt\nePjhh1m6dCm/LM/yVYP9+/fz0ksv0a9fPyZPnswbb7xBamoqs2fP5tlnn6VHjx689957zJ07l7fe\neqtB+98bn15TV9V/Af+qUjbfY3onzml5v4vu2Jr1BcPodUEpj8fFsrBsHHznzGsdHsajo/v4t4LG\nGBNAakq9umLFCkpLS/n000/Zu3dvtU69aurVd999t8Z1e0u9Om/ePKB66tWVK1d6ree0adNo0aIF\nffr0ITY2lpycHKKjo5k7dy6ZmZm0bNmSgwcPAjBo0CB++tOfUlxczKRJk0hISODtt99m7969DBky\nBICSkhKGDas9mVfPnj3p169fpRgKCgrYvn07U6ZMqVjOFxnogmKgXHP4v9F9+MVre4DKOzmqTTi/\nmRjPpMRo/1TMGGOw1KsNVfX5IiLC448/TmxsLKtXr+b06dO0a9cOgGuuuYb09HT++c9/MmPGDO6/\n/37atGnDmDFjvD7bvrb6e8agqnTu3Llelwwaw1KvuiYlRvPo5H60CmuB4By5L5k+gA/mj7IO3Rhj\nahHIqVfXrVuHqnLgwAHy8vLo1asXhYWFdOvWDRHh+eefR1UB+OSTT+jatSuzZ89m5syZfPDBBwwZ\nMoR33nmHQ4cOAc61/ZycnHOuf1RUFN26deP1118HoKysjMzMzHNeT13sSN3DpMRo0gtz+HjRcH9X\nxRhjgoa/U6/Wdk09Ojqa5ORkioqKWL58Oa1atWLu3LlMnTqV5557jvHjx1ccWaelpfHEE08QHh5O\nZGQkq1atokuXLqxYsYLp06dTUlICwCOPPFLrLXrevPzyy8yZM4cFCxZQUlLCjTfeSEJCQiP2SnVS\n/h9KsEhOTtZdu3b5bP12C05wsJiCg8XUONnZ2VxxxRXNsq2abmkLJKWlpZSWllZKvZqTk1NrprZA\nj6kmNbW5iOxW1WQvH6nEjtSNMcYEPEu9Wj+2R4wxxgQ8S71aPzZQzhhjjAkR1qkbY0wAC7ZxT6bh\nmqKtrVM3xpgAFRERQX5+vnXs5wFVJT8/n4iIiEatx66pG2NMgIqJieHIkSOcOHHC59sqLi5udIcS\naIItpoiICGJiGveQVevUjTEmQIWHh9O9e/dm2VZ6enqjsoMFolCMqS52+t0YY4wJEdapG2OMMSHC\nOnVjjDEmRATdY2JF5ATwiQ830Rn4wofr9weLKThYTMEhFGOC0IwrVGK6RFUvqs+CQdep+5qI7Krv\nM3aDhcUUHCym4BCKMUFoxhWKMdXFTr8bY4wxIcI6dWOMMSZEWKde3XJ/V8AHLKbgYDEFh1CMCUIz\nrlCMqVZ2Td0YY4wJEXakbowxxoQI69Q9iMgYEdkvIrki8oC/6+ONiMSKyBYR2Ssi/xGRu9zyTiKy\nSURy3J9RbrmIyDI3riwRSfJY183u8jkicrO/YvKoT5iIfCAi/3DfdxeRDLfufxWRVm75Be77XHf+\npR7r+IVbvl9ERvsnkrNEpKOIvCIi+0QkW0QGB3tbicj/ur97H4nISyISEWxtJSLPichxEfnIo6zJ\n2kVEBorIHvczy0RE/BTT793fvSwReV1EOnrMq3H/e/su9NbGzR2Tx7x7RURFpLP7PijayadU1V7O\nJYgw4CBwGdAKyATi/F0vL3XtBiS505HAASAOeAx4wC1/APidOz0O2AAI8AMgwy3vBBxyf0a501F+\nju0e4EXgH+77tUCqO/1nYI47/TPgz+50KvBXdzrObbsLgO5um4b5Oabngf9xp1sBHYO5rYBo4GOg\ntUcb3RJsbQVcDSQBH3mUNVm7ADvcZcX97Fg/xTQKaOlO/84jphr3P7V8F3pr4+aOyS2PBTbiPLek\nczC1ky9fdqR+1pVArqoeUtUS4GUgxc91qpGqHlPV993pk0A2zhdtCk4HgvtzkjudArygju1ARxHp\nBowGNqnql6r6FbAJGNOMoVQiIjHAeOBZ970A1wCvuItUjak81leAa93lU4CXVfU7Vf0YyMVpW78Q\nkQ44X0orAFS1RFULCPK2wkkG1VpEWgJtgGMEWVup6r+BL6sUN0m7uPPaq+p2dXqOFzzW5TM1xaSq\nb6lqqft2O1CeBszb/q/xu7COv0ef8dJOAE8C9wOeA8OCop18yTr1s6KBPI/3R9yygOaeykwEMoAu\nqnrMnfUZ0MWd9hZboMW8BOePtMx9fyFQ4PGF5Fm/irq78wvd5QMtpu7ACWClOJcVnhWRtgRxW6nq\nUWAx8F+czrwQ2E3wtxU0XbtEu9NVy/3tVpyjUTj3mGr7e2xWIpICHFXVzCqzQqWdGsw69SAmIu2A\nV4G7VfVrz3nuf51Bc2uDiEwAjqvqbn/XpYm1xDl1+CdVTQS+wTmtWyEI2yoK54ioO3Ax0Bb/njXw\niWBrl7qIyK+AUmCNv+vSGCLSBvglMN/fdQlE1qmfdRTnGk25GLcsIIlIOE6HvkZVX3OLP3dPJ+H+\nPO6We4stkGIeClwnIodxTvddAyzFOX3W0l3Gs34VdXfndwDyCayYwPnP/4iqZrjvX8Hp5IO5rUYC\nH6vqCVU9DbyG037B3lbQdO1ylLOnuT3L/UJEbgEmADe4/6zAuceUj/c2bk49cP6hzHS/L2KA90Wk\nK0HeTk3C3xf1A+WFc0R1COeXpXxwSLy/6+WlroJz7WdJlfLfU3mQz2Pu9HgqDx7Z4ZZ3whnwFOW+\nPgY6BUB8wzk7UG4dlQfm/MydvoPKg6/WutPxVB78cwj/D5R7F+jjTi9w2ylo2wr4PvAfnGvpgnNt\n9c5gbCvgUioPKmuydqH6AKxxfoppDLAXuKjKcjXuf2r5LvTWxs0dU5V5hzk7UC5o2sln+8rfFQik\nF87IyQM4Iz9/5e/61FLPYTinBbOAD93XOJxrXmlADvC2xy+tAE+7ce0Bkj3WdSvOAJlcYKa/Y3Pr\nNJyznfpl7h9drvuFcoFbHuG+z3XnX+bx+V+5se4nAEayAgOAXW57veF+qQR1WwG/BfYBHwGr3I4h\nqNoKeAlnTMBpnDMqs5qyXYBkd/8cBP6A+7AvP8SUi3M9ufy74s917X+8fBd6a+PmjqnK/MOc7dSD\nop18+bInyhljjDEhwq6pG2OMMSHCOnVjjDEmRFinbowxxoQI69SNMcaYEGGdujHGGBMirFM3JsiI\nSFE9lrnbffKWt/nPikhcA7adLCLLzmH5dDfbV5abKewPVbKEbTvXOhhjvLNb2owJMiJSpKrt6ljm\nMM49ul/UMC9MVc/4qn5VtpUO3Kequ9w0nY+69fphc2zfmPONHakbE6REZLh7JFyeq32Nm0/65zjP\nZN8iIlvcZYtE5HERyQQGu59L9pj3sIhkish2Eenilv9YnHzpmSLyb49tlue6byciK91c1FkiMqW2\n+qqT8et+4HsiklC+bY/1viMifxORQyKySERuEJEd7vp7+GQnGhNirFM3JrglAnfj5Ma+DBiqqsuA\nT4ERqjrCXa4tTm7pBFXdWmUdbYHtqpoA/Bu4zS2fD4x2y6+rYdu/BgpVtZ+q9gc211VZ9wxBJnB5\nDbMTgNuBK4CbgN6qeiVOKt4761q3McY6dWOC3Q5VPaKqZTiPAL3Uy3JncBIA1aQE+Ic7vdtjHe8B\nfxGR23CeCV7VSJxHcgKgTp7q+hAv5TtV9ZiqfofzyM633PI9eI/LGOPBOnVjgtt3HtNncJJx1KS4\nluvop/Xs4JqKdajq7cCDONmtdovIhY2trIiEAf2A7Bpme8ZS5vG+DO9xGWM8WKduTGg6CUQ2ZgUi\n0kNVM1R1PnCCyqkrATbhZGArXz6qjvWF4wyUy1PVrMbUzRhTM+vUjQlNy4E3ywfKNdDv3UFqHwHb\ncK6Fe1oIRJUPpgNGVFuDY42IZOFkwmoLpDSiTsaYWtgtbcYYY0yIsCN1Y4wxJkRYp26MMcaECOvU\njTHGmBBhnboxxhgTIqxTN8YYY0KEderGGGNMiLBO3RhjjAkR1qkbY4wxIeL/AUFhu3FdllPFAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f59b8e7c290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, Rs[:,1],'b-', label=\"Testing\")\n",
    "ax.plot(dim, R_dir[1]*np.ones(N),'b-', label=\"Testing: baseline\")\n",
    "ax.plot(dim, Rs[:,3],'g-', label=\"Training\")\n",
    "ax.plot(dim, R_dir[3]*np.ones(N),'g-', label=\"Training: baseline\")\n",
    "ax.scatter(dim, Rs[:,1])\n",
    "ax.scatter(dim, Rs[:,3])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Accuracy')\n",
    "ax.set_title('Cross Entropy  Accuracy')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "# ax.set_ylim([0.75,1.01])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YW8dyS/VbvI4ludApC79z7o5TfLYdeDPLE4mIhJjtB7bTdWJXJv0+iasqX8XH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f59bb02c750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, Rs[:,4],'g-', label=\"Training\")\n",
    "ax.plot(dim, R_dir[4]*np.ones(N),'g-', label=\"Training: baseline\")\n",
    "ax.scatter(dim, Rs[:,4])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Time (second)')\n",
    "ax.set_title('Wall Clock Time')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "# ax.set_ylim([0.75,100.01])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Per Dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  2.78242000e-01   1.04400000e-02   2.77033000e-01   1.08000000e-02\n",
      "    2.83466000e+01]\n",
      " [  4.58944000e-02   3.75800000e-03   4.57274000e-02   3.75160000e-03\n",
      "    6.16420000e+00]\n",
      " [  2.16152000e-02   2.34500000e-03   2.15670000e-02   2.34640000e-03\n",
      "    3.34556000e+00]\n",
      " [  3.41058000e-03   7.85800000e-04   3.39704000e-03   7.90920000e-04\n",
      "    6.89494000e-01]\n",
      " [  1.51444000e-03   4.53900000e-04   1.48573000e-03   4.71260000e-04\n",
      "    3.69209000e-01]\n",
      " [  6.69140000e-04   2.62000000e-04   6.40375000e-04   2.73920000e-04\n",
      "    2.33359500e-01]\n",
      " [  2.38922000e-04   1.17300000e-04   2.13870000e-04   1.24724000e-04\n",
      "    1.46449200e-01]\n",
      " [  1.11838000e-04   6.19600000e-05   8.72310000e-05   6.94060000e-05\n",
      "    1.15721000e-01]\n",
      " [  7.90513333e-05   4.12666667e-05   4.97324000e-05   4.92066667e-05\n",
      "    1.12802000e-01]]\n"
     ]
    },
    {
     "data": {
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E0mXA3IiYKemtwG3AcOCfJF0aEW+MiBWSvkryhQHgsraT9crFZ+ObmVlRdXUj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f243ce34710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jks4F5kfEXEnvAn4OjAP+RtI3ImLviHhZ0r+QfGEAOLezs16luDe+mZkVVW/J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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f243ccfddd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NRs = Rs/np.array(dim).reshape(N,1)\n",
    "print NRs\n",
    "\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,0],'b-', label=\"Testing\")\n",
    "ax.scatter(dim, NRs[:,0])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)\n",
    "\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,2],'g-', label=\"Training\")\n",
    "ax.scatter(dim, NRs[:,2])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LC6Y3ASe2su3YVsof4u2XDZaMbxZkZmZ55TsGdpHvE2BmZnnlJKCLnASYmVleOQnoIt8s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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f243c730550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Jpzvg3G4euxp4Ied1HXBYR9tERLOkDcDotPyeNvu23pvgYuALQL+5k6G7A8zM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\nXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f243ef111d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,1],'b-', label=\"Testing\")\n",
    "ax.scatter(dim, NRs[:,1])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)\n",
    "\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,3],'g-', label=\"Training\")\n",
    "ax.scatter(dim, NRs[:,3])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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honkiu8ruxGkEYGTVgJQjExER6ZoSOVA9pIIB5aUMbvogzfYvGkrvZUB5KZee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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f243c8ed810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,4],'g-', label=\"Training\")\n",
    "ax.scatter(dim, NRs[:,4])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Time (second)')\n",
    "ax.set_title('Wall Clock Time')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "# ax.set_ylim([0.75,100.01])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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